UniNorth Student Fit Index — methodology

The Student Fit Index is UniNorth's transparent, evidence-based decision aid for international students researching universities in North Cyprus.

What the Index is

A category-weighted score (0–100) for each university across seven decision-relevant dimensions. Every value carries a source URL, a confidence level, and a verification date. The headline number is computed deterministically from the seven category scores using fixed weights (see below). When data is unavailable for a category, the scorecard is marked partial or insufficient rather than silently treating unknown as zero.

The Index is built to power four product surfaces:

• University profile scorecards — single-university view with category bars, evidence counts, and a confidence badge.
• 2–3 university comparisons — side-by-side rendering with "Best for …" leader tags.
• Infographic-style visual comparisons — shareable cards driven by the same data.
• "Best fit for …" recommendations — surface category-leader tags in topic pages (e.g. /programs/medicine).
• Future personalised ranking — student-quiz-driven re-weighting of the canonical weights, keeping the underlying scorecard immutable.

What the Index is not

• It is not an official academic ranking, and it makes no claim to represent overall university quality.
• It does not score teaching or research output. There are dedicated international rankings (THE, QS, Shanghai) that try, each with their own methodology trade-offs.
• It does not aggregate student reviews. A qualitative "Student Voice Signal" layer is on the roadmap, kept separate from the headline score because review data is subjective and easy to manipulate.
• It does not scrape LinkedIn or any individual alumni profile. Career data, when used, is manual, aggregate, anonymised, and clearly marked beta.

Categories and weights (v1)

#	Category	Weight	Beta
1	Academic / Programme Transparency	18%	—
2	After Graduation Outlook	18%	beta
3	Cost Clarity	16%	—
4	International Student Support	15%	—
5	Campus Life	13%	—
6	City & Lifestyle	12%	—
7	Trust & Safety	8%	—
	Total	100%

Changes to the weights or category set are versioned methodology changes and recorded in the revision log at the bottom of this page.

The Student Voice Signal layer is deliberately excluded from the overall score because qualitative review data is subjective and easy to manipulate. It appears as a sibling on the scorecard, never folded into the headline.

Sub-metrics

Each category aggregates 3–5 sub-metrics. Sub-metrics within a category are averaged with equal weight unless a reviewer-set override applies. A summary:

1. Academic / Programme Transparency

• Target programme availability
• English-taught programme clarity
• Accreditation / recognition evidence
• Curriculum transparency
• Faculty / department page quality

2. After Graduation Outlook (beta)

• Career centre visibility
• Internship / placement information
• Employer or career event evidence
• Alumni visibility
• Public aggregate alumni / career signal — beta only; LinkedIn-style data is never scraped automatically; if used later it must be manual, aggregate, anonymised, and must not expose individual profiles.

3. Cost Clarity

• Tuition fee transparency
• Scholarship clarity
• Dorm / housing cost availability
• Extra fees visibility
• Refund / payment policy clarity

Cost level and cost clarity are different. A university can be expensive and still score well on Cost Clarity if its fees are transparent. The Index does not penalise high tuition; it penalises opaque tuition.

4. International Student Support

• Admission process clarity
• Visa / residence permit guidance
• International office accessibility
• Orientation / onboarding evidence
• Multilingual support

5. Campus Life

• Campus facilities evidence
• Dorms on or near campus
• Library / lab / sports facilities
• Health / medical access
• Food, transport, and student services

6. City & Lifestyle

• Distance to city centre
• Transport / shuttle evidence
• Nearby cafés, shops, restaurants
• Beach / social area access
• General student lifestyle signal

7. Trust & Safety

• Official fee pages available and updated
• Accreditation pages available
• Official contact clarity
• Information consistency across pages/PDFs
• Agent-risk or scam-awareness information

Scoring principles

1. Every score is explainable. Each sub-metric carries evidence: a description, a source URL, a verification date, a confidence level, and reviewer notes.
2. Every score is re-verifiable. A reviewer must be able to reproduce the score from the linked evidence within ten minutes.
3. No invented data. If a fact is not in the evidence, it is not in the score.
4. Unknown is a value, not a zero. An unknown sub-metric or category score is preserved; the headline status drops to partial or insufficient accordingly.
5. Confidence aggregates as the worst-of. A category's confidence is the lowest confidence among its scored sub-metrics — chains are only as strong as their weakest link.
6. Weights are stable; framing is flexible. The headline score uses fixed weights so cross-uni comparison stays fair; personalised views may re-weight at render time without mutating stored data.
7. No "best university" claims unless the methodology and the data both back the exact statement (e.g. "Best for Cost Clarity" only when leading the comparison set with a score ≥ 75).

Confidence levels

Level	Meaning
high	Reviewer captured the value from a primary, dated source on the university's own domain.
medium	Reviewer captured the value from a primary source, but the source is partial or older than 6 months.
low	Reviewer captured a secondary or indirect indicator (third-party page, archived snapshot, agent restatement).
unknown	No usable evidence found yet. Score MUST be null.

Confidence influences:

• The category-level confidence badge (worst of the contributing sub-metrics).
• The "Where review effort is needed" callout on the scorecard.
• Whether the scorecard is ready to publish (we do not publish a scorecard with two or more low-confidence categories without an editorial pass).

How "unknown" data is handled

• Unknown sub-metrics are preserved through aggregation; only scored sub-metrics contribute to the category score.
• A category whose sub-metrics are all unknown is shown as unknown — not as a zero.
• The overall headline is the weighted average of categories that do have a score, divided by the sum of available weights — not by the full 100%.
• The publishing status is computed from coverage, not from headline value:
  • Complete — all 7 categories scored.
  • Partial — at least 60% of the methodology (by weight) covered.
  • Insufficient — below 60% coverage. The headline is hidden, with a clear "not yet enough data" state.

Negative or sensitive claims

Any sub-metric that captures a negative claim about a university (e.g. inconsistent fee disclosure, agent-risk warning) requires:

1. Two independent pieces of evidence, OR
2. A primary-source contradiction the reviewer can quote verbatim.
3. A second reviewer signs off before the scorecard is marked published.

A scorecard may stay partial rather than publish a single-evidence negative claim.

Career and alumni data — safe handling

The "After Graduation Outlook" category is marked beta in v1 because the underlying signals (employer visibility, internship pipelines, alumni visibility) are partial and easy to misread. Specifically:

• LinkedIn or any individual alumni profile is never scraped automatically.
• If LinkedIn-style data is used later, it must be:
  • Manual (collected by a reviewer in an authenticated session, never via automated headless browsers).
  • Aggregate (e.g. "12 of 30 sampled graduates list public-sector roles").
  • Anonymised (no individual identifiable in the published scorecard).
  • Documented (the sampling method, sample size, and date range named in the evidence).
• Until that pipeline exists, the public alumni signal stays unknown for every university.

Editorial disclaimers

When a scorecard surfaces with sample or partial data, the following labels accompany it so readers know what they're looking at:

Sample data. UniNorth scores are only published after source verification and human review. Figures shown before that pass are placeholders, used to validate layout and editorial flow.

Some categories on this scorecard are not yet reviewed. The headline reflects only the categories with data — the full breakdown shows which are covered. We do not silently fill missing categories with zeros.

Beta category. The data behind this score is harder to verify safely. We keep it beta until the verification process is documented and tested across all six universities.

Future roadmap

• Personalised weighting. Student picks 2–3 priorities (e.g. "I care most about Cost Clarity and Trust & Safety"); the page re-weights at render time without mutating stored category scores.
• Student quiz. A short flow that maps quiz answers onto a personalised weight vector, then surfaces matching universities by personalised score.
• Student Voice Signal. A qualitative layer separate from the headline — themed quotes, sourced reviews, never aggregated into the score.
• Verified partner data. When a university opens a verified-partner channel (e.g. confirms fee changes via signed email), the corresponding sub-metrics gain a verified_partner flag and a confidence boost.
• Programmatic vs single uni view. Fold sub-scores into /programs/[slug] pages: which uni leads on which dimension for that subject specifically.

Revision log

• v1.0 (2026-05-07) — initial methodology. Seven categories with weights as published. Preview scorecards shown for layout validation while the verified dataset is built out.